
from collections import OrderedDict
from typing import Mapping

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging


SCREAMING_SNAKE_CASE				:Union[str, Any]									=							logging.get_logger(__name__)

SCREAMING_SNAKE_CASE				:Dict									=							{
    'kssteven/ibert-roberta-base': 'https://huggingface.co/kssteven/ibert-roberta-base/resolve/main/config.json',
    'kssteven/ibert-roberta-large': 'https://huggingface.co/kssteven/ibert-roberta-large/resolve/main/config.json',
    'kssteven/ibert-roberta-large-mnli': (
        'https://huggingface.co/kssteven/ibert-roberta-large-mnli/resolve/main/config.json'
    ),
}





class 				UpperCAmelCase (    __SCREAMING_SNAKE_CASE    ):




	'''simple docstring'''





	snake_case_          =	"ibert"






	def __init__(			self   :					Union[str, Any]				,A   :					Tuple=3_05_22				,A   :					Optional[Any]=7_68				,A   :					List[Any]=12				,A   :					Optional[Any]=12				,A   :					List[str]=30_72				,A   :					Union[str, Any]="gelu"				,A   :					str=0.1				,A   :					int=0.1				,A   :					Dict=5_12				,A   :					str=2				,A   :					Any=0.02				,A   :					str=1E-12				,A   :					List[str]=1				,A   :					str=0				,A   :					Optional[Any]=2				,A   :					Union[str, Any]="absolute"				,A   :					Optional[int]=False				,A   :					Any="none"				,**A   :					str				,):
					super().__init__(pad_token_id=A				,bos_token_id=A				,eos_token_id=A				,**A					)

					__A       =					vocab_size
					__A       =					hidden_size
					__A       =					num_hidden_layers
					__A       =					num_attention_heads
					__A       =					hidden_act
					__A       =					intermediate_size
					__A       =					hidden_dropout_prob
					__A       =					attention_probs_dropout_prob
					__A       =					max_position_embeddings
					__A       =					type_vocab_size
					__A       =					initializer_range
					__A       =					layer_norm_eps
					__A       =					position_embedding_type
					__A       =					quant_mode
					__A       =					force_dequant





class 				UpperCAmelCase (    __SCREAMING_SNAKE_CASE    ):




	'''simple docstring'''





	@property
	def 							UpperCamelCase_    (			self   :					Tuple					):
					if self.task == "multiple-choice":
									__A       =					{0: "batch", 1: "choice", 2: "sequence"}
					else:
									__A       =					{0: "batch", 1: "sequence"}
					return OrderedDict(
					    [
					        ("input_ids", dynamic_axis),
					        ("attention_mask", dynamic_axis),
					    ]					)







"""simple docstring"""





import collections
import inspect
import unittest

from transformers import SwinvaConfig
from transformers.testing_utils import require_torch, require_vision, slow, torch_device
from transformers.utils import cached_property, is_torch_available, is_vision_available

from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, _config_zero_init, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
       import torch
       from torch import nn

       from transformers import SwinvaForImageClassification, SwinvaForMaskedImageModeling, SwinvaModel
       from transformers.models.swinva.modeling_swinva import SWINV2_PRETRAINED_MODEL_ARCHIVE_LIST

if is_vision_available():
       from PIL import Image

       from transformers import AutoImageProcessor

class 						lowercase   :





      def __init__(			self ,		lowercase ,		lowercase=13 ,		lowercase=32 ,		lowercase=2 ,		lowercase=3 ,		lowercase=16 ,		lowercase=[1, 2, 1] ,		lowercase=[2, 2, 4] ,		lowercase=2 ,		lowercase=2.0 ,		lowercase=True ,		lowercase=0.0 ,		lowercase=0.0 ,		lowercase=0.1 ,		lowercase="gelu" ,		lowercase=False ,		lowercase=True ,		lowercase=0.02 ,		lowercase=1e-5 ,		lowercase=True ,		lowercase=None ,		lowercase=True ,		lowercase=10 ,		lowercase=8 ,		)						->	Union[str, Any]:
            lowerCAmelCase    		=			parent
            lowerCAmelCase    		=			batch_size
            lowerCAmelCase    		=			image_size
            lowerCAmelCase    		=			patch_size
            lowerCAmelCase    		=			num_channels
            lowerCAmelCase    		=			embed_dim
            lowerCAmelCase    		=			depths
            lowerCAmelCase    		=			num_heads
            lowerCAmelCase    		=			window_size
            lowerCAmelCase    		=			mlp_ratio
            lowerCAmelCase    		=			qkv_bias
            lowerCAmelCase    		=			hidden_dropout_prob
            lowerCAmelCase    		=			attention_probs_dropout_prob
            lowerCAmelCase    		=			drop_path_rate
            lowerCAmelCase    		=			hidden_act
            lowerCAmelCase    		=			use_absolute_embeddings
            lowerCAmelCase    		=			patch_norm
            lowerCAmelCase    		=			layer_norm_eps
            lowerCAmelCase    		=			initializer_range
            lowerCAmelCase    		=			is_training
            lowerCAmelCase    		=			scope
            lowerCAmelCase    		=			use_labels
            lowerCAmelCase    		=			type_sequence_label_size
            lowerCAmelCase    		=			encoder_stride





      def        _snake_case     (			self					)						->	Tuple:
            lowerCAmelCase    		=			floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]					)

            lowerCAmelCase    		=			None
            if self.use_labels:
                  lowerCAmelCase    		=			ids_tensor([self.batch_size] ,		self.type_sequence_label_size					)

            lowerCAmelCase    		=			self.get_config()

            return config, pixel_values, labels





      def        _snake_case     (			self					)						->	str:
            return SwinvaConfig(
                image_size=self.image_size ,		patch_size=self.patch_size ,		num_channels=self.num_channels ,		embed_dim=self.embed_dim ,		depths=self.depths ,		num_heads=self.num_heads ,		window_size=self.window_size ,		mlp_ratio=self.mlp_ratio ,		qkv_bias=self.qkv_bias ,		hidden_dropout_prob=self.hidden_dropout_prob ,		attention_probs_dropout_prob=self.attention_probs_dropout_prob ,		drop_path_rate=self.drop_path_rate ,		hidden_act=self.hidden_act ,		use_absolute_embeddings=self.use_absolute_embeddings ,		path_norm=self.patch_norm ,		layer_norm_eps=self.layer_norm_eps ,		initializer_range=self.initializer_range ,		encoder_stride=self.encoder_stride ,		)





      def        _snake_case     (			self ,		lowercase ,		lowercase ,		lowercase					)						->	Tuple:
            lowerCAmelCase    		=			SwinvaModel(config=lowercase					)
            model.to(lowercase					)
            model.eval()
            lowerCAmelCase    		=			model(lowercase					)

            lowerCAmelCase    		=			((config.image_size // config.patch_size) ** 2) // (4 ** (len(config.depths					) - 1))
            lowerCAmelCase    		=			int(config.embed_dim * 2 ** (len(config.depths					) - 1)					)

            self.parent.assertEqual(result.last_hidden_state.shape ,		(self.batch_size, expected_seq_len, expected_dim)					)





      def        _snake_case     (			self ,		lowercase ,		lowercase ,		lowercase					)						->	str:
            lowerCAmelCase    		=			SwinvaForMaskedImageModeling(config=lowercase					)
            model.to(lowercase					)
            model.eval()
            lowerCAmelCase    		=			model(lowercase					)
            self.parent.assertEqual(
                result.logits.shape ,		(self.batch_size, self.num_channels, self.image_size, self.image_size)					)

            # test greyscale images
            lowerCAmelCase    		=			1
            lowerCAmelCase    		=			SwinvaForMaskedImageModeling(lowercase					)
            model.to(lowercase					)
            model.eval()

            lowerCAmelCase    		=			floats_tensor([self.batch_size, 1, self.image_size, self.image_size]					)
            lowerCAmelCase    		=			model(lowercase					)
            self.parent.assertEqual(result.logits.shape ,		(self.batch_size, 1, self.image_size, self.image_size)					)





      def        _snake_case     (			self ,		lowercase ,		lowercase ,		lowercase					)						->	Dict:
            lowerCAmelCase    		=			self.type_sequence_label_size
            lowerCAmelCase    		=			SwinvaForImageClassification(lowercase					)
            model.to(lowercase					)
            model.eval()
            lowerCAmelCase    		=			model(lowercase ,		labels=lowercase					)
            self.parent.assertEqual(result.logits.shape ,		(self.batch_size, self.type_sequence_label_size)					)





      def        _snake_case     (			self					)						->	Tuple:
            lowerCAmelCase    		=			self.prepare_config_and_inputs()
            lowerCAmelCase		,						lowerCAmelCase		,						lowerCAmelCase    		=			config_and_inputs
            lowerCAmelCase    		=			{"""pixel_values""": pixel_values}
            return config, inputs_dict







@require_torch
class 						lowercase   (   _UpperCAmelCase	,    _UpperCAmelCase	,    unittest.TestCase   ):
      _SCREAMING_SNAKE_CASE							      = (
          (SwinvaModel, SwinvaForImageClassification, SwinvaForMaskedImageModeling) if is_torch_available() else ()
      )
      _SCREAMING_SNAKE_CASE							      = (
          {'feature-extraction': SwinvaModel, 'image-classification': SwinvaForImageClassification}
          if is_torch_available()
          else {}
      )

      _SCREAMING_SNAKE_CASE							      = False
      _SCREAMING_SNAKE_CASE							      = False
      _SCREAMING_SNAKE_CASE							      = False
      _SCREAMING_SNAKE_CASE							      = False





      def        _snake_case     (			self					)						->	Any:
            lowerCAmelCase    		=			SwinvaModelTester(self					)
            lowerCAmelCase    		=			ConfigTester(self ,		config_class=lowercase ,		embed_dim=37					)





      def        _snake_case     (			self					)						->	Tuple:
            self.config_tester.create_and_test_config_to_json_string()
            self.config_tester.create_and_test_config_to_json_file()
            self.config_tester.create_and_test_config_from_and_save_pretrained()
            self.config_tester.create_and_test_config_with_num_labels()
            self.config_tester.check_config_can_be_init_without_params()
            self.config_tester.check_config_arguments_init()





      def        _snake_case     (			self					)						->	int:
            lowerCAmelCase    		=			self.model_tester.prepare_config_and_inputs()
            self.model_tester.create_and_check_model(*lowercase					)





      @unittest.skip(reason="""Got `CUDA error: misaligned address` with PyTorch 2.0.0."""					)
      def        _snake_case     (			self					)						->	str:
            pass





      @unittest.skip(reason="""Swinv2 does not use inputs_embeds"""					)
      def        _snake_case     (			self					)						->	str:
            pass





      def        _snake_case     (			self					)						->	Tuple:
            lowerCAmelCase		,						lowerCAmelCase    		=			self.model_tester.prepare_config_and_inputs_for_common()

            for model_class in self.all_model_classes:
                  lowerCAmelCase    		=			model_class(lowercase					)
                  self.assertIsInstance(model.get_input_embeddings() ,		(nn.Module)					)
                  lowerCAmelCase    		=			model.get_output_embeddings()
                  self.assertTrue(x is None or isinstance(lowercase ,		nn.Linear					)					)





      def        _snake_case     (			self					)						->	Optional[int]:
            lowerCAmelCase		,						lowerCAmelCase    		=			self.model_tester.prepare_config_and_inputs_for_common()

            for model_class in self.all_model_classes:
                  lowerCAmelCase    		=			model_class(lowercase					)
                  lowerCAmelCase    		=			inspect.signature(model.forward					)
                  # signature.parameters is an OrderedDict => so arg_names order is deterministic
                  lowerCAmelCase    		=			[*signature.parameters.keys()]

                  lowerCAmelCase    		=			["""pixel_values"""]
                  self.assertListEqual(arg_names[:1] ,		lowercase					)





      def        _snake_case     (			self					)						->	Tuple:
            lowerCAmelCase		,						lowerCAmelCase    		=			self.model_tester.prepare_config_and_inputs_for_common()
            lowerCAmelCase    		=			True

            for model_class in self.all_model_classes:
                  lowerCAmelCase    		=			True
                  lowerCAmelCase    		=			False
                  lowerCAmelCase    		=			True
                  lowerCAmelCase    		=			model_class(lowercase					)
                  model.to(lowercase					)
                  model.eval()
                  with torch.no_grad():
                        lowerCAmelCase    		=			model(**self._prepare_for_class(lowercase ,		lowercase					)					)
                  lowerCAmelCase    		=			outputs.attentions
                  lowerCAmelCase    		=			len(self.model_tester.depths					)
                  self.assertEqual(len(lowercase					) ,		lowercase					)

                  # check that output_attentions also work using config
                  del inputs_dict["output_attentions"]
                  lowerCAmelCase    		=			True
                  lowerCAmelCase    		=			config.window_size**2
                  lowerCAmelCase    		=			model_class(lowercase					)
                  model.to(lowercase					)
                  model.eval()
                  with torch.no_grad():
                        lowerCAmelCase    		=			model(**self._prepare_for_class(lowercase ,		lowercase					)					)
                  lowerCAmelCase    		=			outputs.attentions
                  self.assertEqual(len(lowercase					) ,		lowercase					)

                  self.assertListEqual(
                      list(attentions[0].shape[-3:]					) ,		[self.model_tester.num_heads[0], window_size_squared, window_size_squared] ,		)
                  lowerCAmelCase    		=			len(lowercase					)

                  # Check attention is always last and order is fine
                  lowerCAmelCase    		=			True
                  lowerCAmelCase    		=			True
                  lowerCAmelCase    		=			model_class(lowercase					)
                  model.to(lowercase					)
                  model.eval()
                  with torch.no_grad():
                        lowerCAmelCase    		=			model(**self._prepare_for_class(lowercase ,		lowercase					)					)

                  if hasattr(self.model_tester ,		"""num_hidden_states_types"""					):
                        lowerCAmelCase    		=			self.model_tester.num_hidden_states_types
                  else:
                        # also another +1 for reshaped_hidden_states
                        lowerCAmelCase    		=			2
                  self.assertEqual(out_len + added_hidden_states ,		len(lowercase					)					)

                  lowerCAmelCase    		=			outputs.attentions

                  self.assertEqual(len(lowercase					) ,		lowercase					)

                  self.assertListEqual(
                      list(self_attentions[0].shape[-3:]					) ,		[self.model_tester.num_heads[0], window_size_squared, window_size_squared] ,		)





      def        _snake_case     (			self ,		lowercase ,		lowercase ,		lowercase ,		lowercase					)						->	List[str]:
            lowerCAmelCase    		=			model_class(lowercase					)
            model.to(lowercase					)
            model.eval()

            with torch.no_grad():
                  lowerCAmelCase    		=			model(**self._prepare_for_class(lowercase ,		lowercase					)					)

            lowerCAmelCase    		=			outputs.hidden_states

            lowerCAmelCase    		=			getattr(
                self.model_tester ,		"""expected_num_hidden_layers""" ,		len(self.model_tester.depths					) + 1					)
            self.assertEqual(len(lowercase					) ,		lowercase					)

            # Swinv2 has a different seq_length
            lowerCAmelCase    		=			(
                config.patch_size
                if isinstance(config.patch_size ,		collections.abc.Iterable					)
                else (config.patch_size, config.patch_size)
            )

            lowerCAmelCase    		=			(image_size[1] // patch_size[1]) * (image_size[0] // patch_size[0])

            self.assertListEqual(
                list(hidden_states[0].shape[-2:]					) ,		[num_patches, self.model_tester.embed_dim] ,		)

            lowerCAmelCase    		=			outputs.reshaped_hidden_states
            self.assertEqual(len(lowercase					) ,		lowercase					)

            lowerCAmelCase		,						lowerCAmelCase		,						lowerCAmelCase		,						lowerCAmelCase    		=			reshaped_hidden_states[0].shape
            lowerCAmelCase    		=			(
                reshaped_hidden_states[0].view(lowercase ,		lowercase ,		height * width					).permute(0 ,		2 ,		1					)
            )
            self.assertListEqual(
                list(reshaped_hidden_states.shape[-2:]					) ,		[num_patches, self.model_tester.embed_dim] ,		)





      def        _snake_case     (			self					)						->	Dict:
            lowerCAmelCase		,						lowerCAmelCase    		=			self.model_tester.prepare_config_and_inputs_for_common()

            lowerCAmelCase    		=			(
                self.model_tester.image_size
                if isinstance(self.model_tester.image_size ,		collections.abc.Iterable					)
                else (self.model_tester.image_size, self.model_tester.image_size)
            )

            for model_class in self.all_model_classes:
                  lowerCAmelCase    		=			True
                  self.check_hidden_states_output(lowercase ,		lowercase ,		lowercase ,		lowercase					)

                  # check that output_hidden_states also work using config
                  del inputs_dict["output_hidden_states"]
                  lowerCAmelCase    		=			True

                  self.check_hidden_states_output(lowercase ,		lowercase ,		lowercase ,		lowercase					)





      def        _snake_case     (			self					)						->	int:
            lowerCAmelCase		,						lowerCAmelCase    		=			self.model_tester.prepare_config_and_inputs_for_common()
            lowerCAmelCase    		=			3

            lowerCAmelCase    		=			(
                self.model_tester.image_size
                if isinstance(self.model_tester.image_size ,		collections.abc.Iterable					)
                else (self.model_tester.image_size, self.model_tester.image_size)
            )
            lowerCAmelCase    		=			(
                config.patch_size
                if isinstance(config.patch_size ,		collections.abc.Iterable					)
                else (config.patch_size, config.patch_size)
            )

            lowerCAmelCase    		=			image_size[0] + patch_size[0] - (image_size[0] % patch_size[0])
            lowerCAmelCase    		=			image_size[1] + patch_size[1] - (image_size[1] % patch_size[1])

            for model_class in self.all_model_classes:
                  lowerCAmelCase    		=			True
                  self.check_hidden_states_output(lowercase ,		lowercase ,		lowercase ,		(padded_height, padded_width)					)

                  # check that output_hidden_states also work using config
                  del inputs_dict["output_hidden_states"]
                  lowerCAmelCase    		=			True
                  self.check_hidden_states_output(lowercase ,		lowercase ,		lowercase ,		(padded_height, padded_width)					)





      def        _snake_case     (			self					)						->	Any:
            lowerCAmelCase    		=			self.model_tester.prepare_config_and_inputs()
            self.model_tester.create_and_check_for_masked_image_modeling(*lowercase					)





      def        _snake_case     (			self					)						->	Tuple:
            lowerCAmelCase    		=			self.model_tester.prepare_config_and_inputs()
            self.model_tester.create_and_check_for_image_classification(*lowercase					)





      @slow
      def        _snake_case     (			self					)						->	Any:
            for model_name in SWINV2_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
                  lowerCAmelCase    		=			SwinvaModel.from_pretrained(lowercase					)
                  self.assertIsNotNone(lowercase					)





      def        _snake_case     (			self					)						->	Any:
            lowerCAmelCase		,						lowerCAmelCase    		=			self.model_tester.prepare_config_and_inputs_for_common()

            lowerCAmelCase    		=			_config_zero_init(lowercase					)
            for model_class in self.all_model_classes:
                  lowerCAmelCase    		=			model_class(config=lowercase					)
                  for name, param in model.named_parameters():
                        if "embeddings" not in name and "logit_scale" not in name and param.requires_grad:
                              self.assertIn(
                                  ((param.data.mean() * 1e9).round() / 1e9).item() ,		[0.0, 1.0] ,		msg=f'Parameter {name} of model {model_class} seems not properly initialized' ,		)







@require_vision
@require_torch
class 						lowercase   (   unittest.TestCase   ):





      @cached_property
      def        _snake_case     (			self					)						->	Any:
            return (
                AutoImageProcessor.from_pretrained("""microsoft/swinv2-tiny-patch4-window8-256"""					)
                if is_vision_available()
                else None
            )





      @slow
      def        _snake_case     (			self					)						->	Tuple:
            lowerCAmelCase    		=			SwinvaForImageClassification.from_pretrained("""microsoft/swinv2-tiny-patch4-window8-256"""					).to(
                lowercase					)
            lowerCAmelCase    		=			self.default_image_processor

            lowerCAmelCase    		=			Image.open("""./tests/fixtures/tests_samples/COCO/000000039769.png"""					)
            lowerCAmelCase    		=			image_processor(images=lowercase ,		return_tensors="""pt"""					).to(lowercase					)

            # forward pass
            with torch.no_grad():
                  lowerCAmelCase    		=			model(**lowercase					)

            # verify the logits
            lowerCAmelCase    		=			torch.Size((1, 1_000)					)
            self.assertEqual(outputs.logits.shape ,		lowercase					)
            lowerCAmelCase    		=			torch.tensor([-0.3_947, -0.4_306, 0.0_026]					).to(lowercase					)
            self.assertTrue(torch.allclose(outputs.logits[0, :3] ,		lowercase ,		atol=1e-4					)					)






